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Quality improvement programs such as lean manufacturing, Six-Sigma, and TQM depend on the accurate collection and analysis of relevant data. When successful, these programs lead to continuous process improvement, confident decision making, quick response to problems, and significant cost reductions. Manufacturing Analytics is essential to this process.

NWA Manufacturing AnalyticsOver the past several years, manufacturers have made significant investments in systems that collect, manage, and report data about their processes. All of these systems, from DCS and SCADA/HMI on the plant floor, to manufacturing execution (MES), laboratory (LIMS), supply chain (SCM) and enterprise (ERP) applications either create, collect, store, use, or interact with data that represents or affects some element of the manufacturing process.

Today's challenge in manufacturing is not getting information about a process. Rather, the puzzle that manufacturers want to solve is how to get more value from all the data that is already being collected. Their goal is to provide process managers with the information they need to better understand their processes, identify improvement opportunities, and make confident decisions that will improve yields and reduce costs. Quality improvement programs such as lean manufacturing, Six-Sigma, and TQM depend on the accurate collection and analysis of relevant data. When successful, these programs lead to continuous process improvement, confident decision making, quick response to problems, and significant cost reductions. Manufacturing Analytics is essential to this process.

Manufacturing Analytics enables the shift from reactive to predictive process management by detecting potential problems before they affect the process, lower quality, and increase costs. Manufacturers can then reach the ultimate goal of a stable, well understood process. Using Manufacturing Analytics will deliver statistics-based process understanding and make KPIs (Key Performance Indicators) more sensitive to their underlying components and the processes they are measuring allowing managers to react more quickly as conditions and results change.

Manufacturing Analytics is the aggregation, analysis, and role-based visualization and reporting of data representing the manufacturing process. The emergence of technology standards such as OPC, XML, and Web Services combined with the broad adoption of industry-driven standards such as S88 and S95 creates system interoperability and enables Manufacturing Analytics applications to acquire the data they need and deliver the results where they are most effective. Good Manufacturing Analytics applications connect to disparate sources of data, aggregate and analyze that data, apply statistical and rule-based methods, and deliver role-based output tailored to the needs of each user. By applying well-established statistical techniques used by SPC and Six Sigma programs, Manufacturing Analytics can be implemented at the plant level and used throughout the enterprise.

Proper application of Manufacturing Analytics enables users to separate signals from noise, identify sources of excessive variation, reveal opportunities for improvement, and detect potential problems before they affect process quality or cost. The result is stable, well understood processes, and a shift from reactive to predictive behavior.

Northwest Analytical (NWA) has been delivering advanced SPC solutions for over 20 years. Our SPC Quality Suite is used by over 5000 companies worldwide for plant floor data collection, off-line process data charting and analysis, and web-based reporting. Both end-user organizations and OEM's have used our embedded SPC products to build process data analysis into larger systems for laboratory, plant, or corporate information systems. NWA has both the breadth of manufacturing expertise and the depth of statistics and analytical capability to bring a new generation of Manufacturing Analytics to companies across all industries. Manufacturing Analytics applications from NWA:

Are based on proven statistical methodsConnect to virtually any data sourceIntegrate with existing systemsDeliver clear, easy to interpret resultsAdhere to current and emerging standards for interoperabilityWhen integrated with existing manufacturing systems, NWA software:

Improves process visualization and understandingDetects significant events and problemsIdentifies sources of variationCommunicates key quality informationSupports confident decision makingThe resulting systems:

Drive continuous process improvementSupport development of best practicesMove from reactive to predictive process management Lower costs and increase yield

Published on www.softwarereviewblog.com

Article Tags: Manufacturing Analytics Applications, Manufacturing Analytics, From Reactive, Analytics Applications

Source: Free Articles from ArticlesFactory.com

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